Coarse-grained molecular simulation of extracellular vesicle squeezing for drug loading.

In recent years, extracellular vesicles have become promising carriers as next-generation drug delivery platforms. Effective loading of exogenous cargos without compromising the extracellular vesicle membrane is a major challenge. Rapid squeezing through nanofluidic channels is a widely used approach to load exogenous cargoes into the EV through the nanopores generated temporarily on the membrane. However, the exact mechanism and dynamics of nanopore opening, as well as cargo loading through nanopores during the squeezing process remains unknown and it is impossible to visualize or quantify it experimentally due to the small size of the EV and the fast transient process. This paper developed a systemic algorithm to simulate nanopore formation and predict drug loading during extracellular vesicle (EV) squeezing by leveraging the power of coarse-grain (CG) molecular dynamics simulations with fluid dynamics. The EV CG beads are coupled with implicit the fluctuating lattice Boltzmann solvent. The effects of EV properties and various squeezing test parameters, such as EV size, flow velocity, channel width, and length, on pore formation and drug loading efficiency are analyzed. Based on the simulation results, a phase diagram is provided as a design guide for nanochannel geometry and squeezing velocity to generate pores on the membrane without damaging the EV. This method can be utilized to optimize the nanofluidic device configuration and flow setup to obtain desired drug loading into EVs.

Adaptable Microfluidic Vessel-on-a-Chip Platform for Investigating Tumor Metastatic Transport in Bloodstream.

Cancer metastasis counts for 90% of cancer fatalities, and its development process is still a mystery. The dynamic process of tumor metastatic transport in the blood vessel is not well understood, in which some biomechanical factors, such as shear stress and various flow patterns, may have significant impacts. Here, we report a microfluidic vessel-on-a-chip platform for recapitulating several key metastatic steps of tumor cells in blood vessels on the same chip, including intravasation, circulating tumor cell (CTC) vascular adhesion, and extravasation. Due to its excellent adaptability, our system can reproduce various microenvironments to investigate the specific interactions between CTCs and blood vessels. On the basis of this platform, effects of important biomechanical factors on CTC adhesion such as vascular surface properties and vessel geometry-dependent hemodynamics were specifically inspected. We demonstrated that CTC adhesion is more likely to occur under certain mechano-physiological situations, such as vessels with vascular glycocalyx (VGCX) shedding and hemodynamic disturbances. Finally, computational models of both the fluidic dynamics in vessels and CTC adhesion were established based on the confocal scanned 3D images. The modeling results are believed to provide insights into exploring tumor metastasis progression and inspire new ideas for anticancer therapy development.

[WITHDRAWN] MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems.

This paper has been withdrawn by Khayrul Islam.

Microphysiologically Engineered Vessel-Tumor Model to Investigate Vascular Transport Dynamics of Immune Cells.

Cancer immunotherapy has emerged as a promising therapeutic strategy to combat cancer effectively. However, it is hard to observe and quantify how this in vivo process happens. Three-dimensional (3D) microfluidic vessel-tumor models offer valuable capability to study how immune cells transport during cancer progression. We presented an advanced 3D vessel-supported tumor model consisting of the endothelial lumen and vessel network for the study of T cells' transportation. The process of T cell transport through the vessel network and interaction with tumor spheroids was represented and monitored in vitro. Specifically, we demonstrate that the endothelial glycocalyx serving in the T cells' transport can influence the endothelium-immune interaction. Furthermore, after vascular transport, how programmed cell death protein 1 (PD-1) immune checkpoint inhibition influences the delivered activated-T cells on tumor killing was evaluated. Our in vitro vessel-tumor model provides a microphysiologically engineered platform to represent T cell vascular transportation during tumor immunotherapy. The reported innovative vessel-tumor platform is believed to have the potential to explore the tumor-induced immune response mechanism and preclinically evaluate immunotherapy's effectiveness.

Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. METHODS: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. RESULTS: After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. CONCLUSION: Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal.

Microfluidic Droplet-Assisted Fabrication of Vessel-Supported Tumors for Preclinical Drug Discovery.

High-fidelity in vitro tumor models are important for preclinical drug discovery processes. Currently, the most commonly used model for in vitro drug testing remains the two-dimensional (2D) cell monolayer. However, the natural in vivo tumor microenvironment (TME) consists of extracellular matrix (ECM), supporting stromal cells and vasculature. They not only participate in the progression of tumors but also hinder drug delivery and effectiveness on tumor cells. Here, we report an integrated engineering system to generate vessel-supported tumors for preclinical drug screening. First, gelatin-methacryloyl (GelMA) hydrogel was selected to mimic tumor extracellular matrix (ECM). HCT-116 tumor cells were encapsulated into individual micro-GelMA beads with microfluidic droplet technique to mimic tumor-ECM interactions in vitro. Then, normal human lung fibroblasts were mingled with tumor cells to imitate the tumor-stromal interaction. The tumor cells and fibroblasts reconstituted in the individual GelMA microbead and formed a biomimetic heterotypic tumor model with a core-shell structure. Next, the cell-laden beads were consociated into a functional on-chip vessel network platform to restore the tumor-tumor microenvironment (TME) interaction. Afterward, the anticancer drug paclitaxel was tested on the individual and vessel-supported tumor models. It was demonstrated that the blood vessel-associated TME conferred significant additional drug resistance in the drug screening experiment. The reported system is expected to enable the large-scale fabrication of vessel-supported heterotypic tumor models of various cellular compositions. It is believed to be promising for the large-scale fabrication of biomimetic in vitro tumor models and may be valuable for improving the efficiency of preclinical drug discovery processes.

Acoustofluidic Engineering of Functional Vessel-on-a-Chip.

Construction of in vitro vascular models is of great significance to various biomedical research, such as pharmacokinetics and hemodynamics, and thus is an important direction in the tissue engineering field. In this work, a standing surface acoustic wave field was constructed to spatially arrange suspended endothelial cells into a designated acoustofluidic pattern. The cell patterning was maintained after the acoustic field was withdrawn within the solidified hydrogel. Then, interstitial flow was provided to activate vessel tube formation. In this way, a functional vessel network with specific vessel geometry was engineered on-chip. Vascular function, including perfusability and vascular barrier function, was characterized by microbead loading and dextran diffusion, respectively. A computational atomistic simulation model was proposed to illustrate how solutes cross the vascular membrane lipid bilayer. The reported acoustofluidic methodology is capable of facile and reproducible fabrication of the functional vessel network with specific geometry and high resolution. It is promising to facilitate the development of both fundamental research and regenerative therapy.

Acoustofluidic Engineering Functional Vessel-on-a-Chip.

Construction of in vitro vascular models is of great significance to various biomedical research, such as pharmacokinetics and hemodynamics, thus is an important direction in tissue engineering. In this work, a standing surface acoustic wave field was constructed to spatially arrange suspended endothelial cells into a designated patterning. The cell patterning was maintained after the acoustic field was withdrawn by the solidified hydrogel. Then, interstitial flow was provided to activate vessel tube formation. Thus, a functional vessel-on-a-chip was engineered with specific vessel geometry. Vascular function, including perfusability and vascular barrier function, was characterized by beads loading and dextran diffusion, respectively. A computational atomistic simulation model was proposed to illustrate how solutes cross vascular lipid bilayer. The reported acoustofluidic methodology is capable of facile and reproducible fabrication of functional vessel network with specific geometry. It is promising to facilitate the development of both fundamental research and regenerative therapy.

An Atomistic Study of the Tensile Deformation of Carbon Nanotube-Polymethylmethacrylate Composites.

There has been growing interest in polymer/carbon nanotube (CNT) composites due to an exceptional enhancement in mechanical, structural, thermal, and electronic properties resulting from a small percentage of CNTs. However, the performance of these composites is influenced by the type of polymer used. PMMA is a polymer of particular interest among many other polymers because of its biomaterial applications due to its biocompatibility, non-toxicity, and non-biodegradability. In this research, we utilized a reactive force field to conduct molecular dynamics simulations to investigate changes in the mechanical properties of single-walled carbon nanotube (SWCNT)-reinforced Poly (methyl methacrylate) (PMMA) matrix composites. To explore the potential of SWCNT-reinforced PMMA composites in these applications, we conducted simulations with varying CNT diameters (0.542-1.08 nm), CNT volume fractions (8.1-16.5%), and temperatures (100 K-700 K). We also analyzed the dependence of Young's modulus and interaction energy with different CNT diameters, along with changes in fracture toughness with varying temperatures. Our findings suggest that incorporating a small amount of SWCNT into the PMMA polymer matrix could significantly enhance the mechanical properties of the resulting composite. It is also found that the double-walled carbon nanotube has roughly twice the tensile strength of SWCNT, while maintaining the same simulation cell dimensions.

Rapid prototyping of high-resolution large format microfluidic device through maskless image guided in-situ photopolymerization.

Microfluidic devices have found extensive applications in mechanical, biomedical, chemical, and materials research. However, the high initial cost, low resolution, inferior feature fidelity, poor repeatability, rough surface finish, and long turn-around time of traditional prototyping methods limit their wider adoption. In this study, a strategic approach to a deterministic fabrication process based on in-situ image analysis and intermittent flow control called image-guided in-situ maskless lithography (IGIs-ML), has been proposed to overcome these challenges. By using dynamic image analysis and integrated flow control, IGIs-ML provides superior repeatability and fidelity of densely packed features across a large area and multiple devices. This general and robust approach enables the fabrication of a wide variety of microfluidic devices and resolves critical proximity effect and size limitations in rapid prototyping. The affordability and reliability of IGIs-ML make it a powerful tool for exploring the design space beyond the capabilities of traditional rapid prototyping.

Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

Background: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. Methods: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. Results: After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. Conclusion: Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. With ongoing advancements in model algorithms and their integration into various imaging systems, deep learning stands poised to become an invaluable tool, significantly impacting stem cell research.